Naval Research Laboratory, Center for Bio/Molecular Science and Engineering, Washington, DC, USA.
Sci Rep. 2024 Aug 17;14(1):19074. doi: 10.1038/s41598-024-70101-6.
Single-domain antibodies (sdAbs) or nanobodies have received widespread attention due to their small size (~ 15 kDa) and diverse applications in bio-derived therapeutics. As many modern biotechnology breakthroughs are applied to antibody engineering and design, nanobody thermostability or melting temperature (T) is crucial for their successful utilization. In this study, we present TEMPRO which is a predictive modeling approach for estimating the T of nanobodies using computational methods. Our methodology integrates various nanobody biophysical features to include Evolutionary Scale Modeling (ESM) embeddings, NetSurfP3 structural predictions, pLDDT scores per sdAb region from AlphaFold2, and each sequence's physicochemical characteristics. This approach is validated with our combined dataset containing 567 unique sequences with corresponding experimental T values from a manually curated internal data and a recently published nanobody database, NbThermo. Our results indicate the efficacy of protein embeddings in reliably predicting the T of sdAbs with mean absolute error (MAE) of 4.03 °C and root mean squared error (RMSE) of 5.66 °C, thus offering a valuable tool for the optimization of nanobodies for various biomedical and therapeutic applications. Moreover, we have validated the models' performance using experimentally determined Ts from nanobodies not found in NbThermo. This predictive model not only enhances nanobody thermostability prediction, but also provides a useful perspective of using embeddings as a tool for facilitating a broader applicability of downstream protein analyses.
单域抗体（sdAbs）或纳米抗体由于其体积小（约 15 kDa），并且在生物衍生治疗中有广泛的应用，因此受到了广泛的关注。随着许多现代生物技术突破应用于抗体工程和设计，纳米抗体的热稳定性或熔点（T）对于它们的成功利用至关重要。在这项研究中，我们提出了 TEMPRO，这是一种使用计算方法估计纳米抗体 T 的预测建模方法。我们的方法集成了各种纳米抗体生物物理特征，包括进化尺度建模（ESM）嵌入、NetSurfP3 结构预测、来自 AlphaFold2 的每个 sdAb 区域的 pLDDT 评分以及每个序列的理化特性。该方法通过我们的综合数据集进行验证，该数据集包含 567 个独特序列，每个序列都有来自手动 curated 内部数据和最近发表的纳米抗体数据库 NbThermo 的相应实验 T 值。我们的结果表明，蛋白质嵌入在可靠地预测 sdAbs 的 T 方面非常有效，平均绝对误差（MAE）为 4.03°C，均方根误差（RMSE）为 5.66°C，因此为优化各种生物医学和治疗应用的纳米抗体提供了有价值的工具。此外，我们还使用在 NbThermo 中未发现的纳米抗体的实验确定的 Ts 验证了模型的性能。该预测模型不仅提高了纳米抗体的热稳定性预测，而且还提供了一个有用的视角，即将嵌入用作促进下游蛋白质分析更广泛适用性的工具。
